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Following the 2007-2009 financial crisis, governments around the world passed laws that marked the beginning of new period of enhanced regulation of the financial industry. These laws called for a myriad of new regulations, which in the U.S. are created through the so-called notice-and-comment process. Through examining the text documents generated through this process, we study the formation of regulations to gain insight into how new regulatory regimes are implemented following major laws like the landmark Dodd-Frank Wall Street Reform and Consumer Protection Act. Due to the variety of constituent preferences and political pressures, we find evidence that the government implements rules strategically to extend the regulatory boundary by first pursuing procedural rules that establish how economic activities will be regulated, followed by specifying who is subject to the procedural requirements. Our findings together with the unique nature of the Dodd-Frank Act translate to a number of stylized facts that should guide development of formal models of the rule-making process. [ABSTRACT FROM AUTHOR]

This paper uses text data mining to identify long-term developments in tourism academic research from the perspectives of thematic focus, geography, and gender of tourism authorship. Abstracts of papers published in the period of 1970–2017 in high-ranking tourist journals were extracted from the Scopus database and served as data source for the analysis. Fourteen subject areas were identified using the Latent Dirichlet Allocation (LDA) text mining approach. LDA integrated with GIS information allowed to obtain geography distribution and trends of scholarly output, while probabilistic methods of gender identification based on social network data mining were used to track gender dynamics with sufficient confidence. The findings indicate that, while all 14 topics have been prominent from the inception of tourism studies to the present day, the geography of scholarship has notably expanded and the share of female authorship has increased through time and currently almost equals that of male authorship. [ABSTRACT FROM AUTHOR]

Interest in applying Big Data to tourism is increasing, and automated sentiment analysis has been used to extract public opinion from various sources. This article evaluates the suitability of different types of automated classifiers for applications typical in tourism, hospitality, and marketing studies by comparing their performance to that of human raters. While the commonly used performance indices suggest that on easier-to-classify data sets machine learning methods demonstrate performance comparable to that by human raters, other performance measures such as Cohen’s kappa show that the results of machine learning are still inferior to manual processing. On more difficult and noisy data sets, automated analysis has poorer performance than human raters. The article discusses issues pertinent to selection of appropriate sentiment analysis software and offers a word of caution against using automated classifiers uncritically. [ABSTRACT FROM AUTHOR]

Graphical abstract Location and density of attraction reviews in the study area. Highlights • Segmentation of tourists via attractions visited is conducted with network analysis. • Online reviews are treated as proxy data for tourists' interests and behavior. • A posteriori segmentation exhibits geographical separation of attractions. • Mixed segmentation describes attractions in terms of tourists' interests and age. • Network solutions verified via comparisons with randomly simulated networks. Abstract The study contributes to the tourism literature by demonstrating an approach to segmenting tourists using network analysis with user-generated content. Online reviews of destination attractions are considered as a proxy for visitation data reflective of tourists' interests. The connectivity between attractions is represented with a network of links created by tourists visiting and reviewing multiple attractions. Attraction clusters are revealed by segmenting this network using network analysis tools. Two segmentation solutions are provided: a posteriori, in which only review information is taken into account, and mixed, in which tourist groups are defined a priori by their travel interests and age, and this information is combined with visitation information. The findings are validated using geovisualization and by comparing them with randomly simulated models. [ABSTRACT FROM AUTHOR]

Mega sports events create multiple benefits for the host country but can also bring into focus the political and social problems. This study provides a comprehensive description of the public discourse about Sochi 2014 Winter Olympics on Twitter in two languages, Russian and English. The former represents the perspective of hosts and the latter – that of the guests. The study traces the temporal dynamics of the most salient issues and conducts sentiment analysis of public attitudes. It also examines whether sentiments toward the Games changed as the event unfolded, that is, whether the event succeeded in creating a more positive image of the Games. It was found that while the positive attitudes expressed in the tweets about the Sochi Olympics improved throughout the course of the Games, this improvement was practically significant only for the hosts' segment of the sample, with much smaller improvement in the guests’ segment. [ABSTRACT FROM AUTHOR]

Web based crowdsourcing has become an importantmethod of environmental data processing. Two alternatives are widely used today by researchers in various fields: paid data processing mediated by for-profit businesses such as Amazon's Mechanical Turk, and volunteer data processing conducted by amateur citizen-scientists. While the first option delivers results much faster, it is not quite clear how it compares with volunteer processing in terms of quality. This study compares volunteer and paid processing of social media data originating from climate change discussions on Twitter. The same sample of Twitter messages discussing climate change was offered for processing to the volunteer workers through the Climate Tweet project, and to the paid workers through the Amazon MTurk platform. We found that paid crowdsourcing required the employment of a high redundancy data processing design to obtain quality that was comparable with volunteered processing. Among the methods applied to improve data processing accuracy, limiting the geographical locations of the paid workers appeared the most productive. Conversely, we did not find significant geographical differences in the accuracy of data processed by volunteer workers. We suggest that the main driver of the found pattern is the differences in familiarity of the paid workers with the research topic. [ABSTRACT FROM AUTHOR]